A Label Ranking approach for selecting rankings of Collaborative Filtering algorithms

• Type: Article in International Conference Proceedings Book
• Date: 2018

Authors

Tiago Cunha (Author) Other; Carlos Soares (Author) FEUP; André C. P. L. F. de Carvalho (Author) Other

Conference proceedings International

Title: Proceedings of the 33rd Annual ACM Symposium on Applied Computing, SAC 2018, Pau, France, April 09-13, 2018

Pages: 1393-1395

33rd Annual ACM Symposium on Applied Computing, SAC 2018

9 April 2018 through 13 April 2018

Indexing

ISI Web of Knowledge - 4 Citations

Scopus - 7 Citations

Other information

Authenticus ID: P-00N-ZQT

DOI: 10.1145/3167132.3167418

Abstract (EN): The large amount of Recommender System algorithms makes the selection of the most suitable algorithm for a new dataset a difficult task. Metalearning has been successfully used to deal with this problem. It works by mapping dataset characteristics with the predictive performance obtained by a set of algorithms. The models built on this data are capable of predicting the best algorithm for a new dataset. However, typical approaches try only to predict the best algorithm, overlooking the performance of others. This study focus on the use of Metalearning to select the best ranking of CF algorithms for a new recommendation dataset. The contribution lies in the formalization and experimental validation of using Label Ranking to select a ranked list of algorithms. The experimental procedure proves the superior performance of the proposed approach regarding both ranking accuracy and impact on the baselevel performance. Furthermore, it draws and compares the knowledge regarding metafeature importance for both classification and Label Ranking tasks in order to provide guidelines for the design of algorithms in the Recommender System community.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 3